Predict Disease Progression Research Articles

Physiopathological Aspects of Synovial Fluid and Membrane in Psoriatic Arthritis

Background: Psoriatic arthritis (PsA) is a chronic inflammatory arthropathy characterized by a variety of clinical manifestations, mainly affecting joints and entheses, but also skin, nails, the eye, and the intestine. Objectives: In this review, we describe the essential characteristics of both synovial membranes and synovial fluid (SF) in PsA. Similarly to other inflammatory arthritis, the histological peculiarities in PsA synovitis are lining hyperplasia, neoangiogenesis, and sublining infiltration by immune cells and inflammatory mediators. Synovial effusions are frequent in PsA patients and SF analysis allows us to determine the pathological process occurring in the joint. Routine examinations help clinicians in defining the inflammatory status and possibly the detection of specific cell subsets. In addition, pathogenic crystals including monosodium urate and calcium pyrophosphate may be found in PsA SF. Conclusions: SF represents a potential substrate to identify the biomarkers that are useful to predict disease progression and response to medications in PsA patients, thus guiding the choice of appropriate and tailored pharmacological treatment.

BIG DATA AND CHRONIC DISEASE MANAGEMENT THROUGH PATIENT MONITORING AND TREATMENT WITH DATA ANALYTICS

This systematic review investigates the role of predictive analytics in chronic disease management, focusing on its capacity to predict disease progression, reduce hospitalizations, and enhance patient outcomes. A total of 35 peer-reviewed articles were analyzed to evaluate how predictive models, utilizing data from electronic health records (EHRs), wearable devices, and real-time monitoring systems, are applied in managing chronic diseases such as diabetes and cardiovascular conditions. The findings indicate that predictive models have significantly improved early disease detection accuracy, with studies showing improved forecasting of heart failure exacerbations and notable reductions in hospital readmissions due to timely interventions. Additionally, many studies reported a reduction in healthcare utilization through predictive analytics-driven early interventions. However, challenges regarding data quality and model accuracy were frequently cited, particularly concerning data integration and harmonization across various healthcare systems. Ethical and privacy concerns, including data security and algorithmic bias, were also highlighted, underscoring the need to address these issues for responsible and equitable use of predictive analytics. This review affirms the growing impact of predictive analytics in chronic disease management, while calling for advancements in data management and ethical frameworks to maximize its potential.

Disorders in cytokine synthesis in women with uterine fibroids

In the structure of gynecological diseases, uterine fibroids occupy an “honorable” second place after inflammatory processes of the genital organs, and its share reaches 40%. Uterine fibroids are a benign monoclonal tumor that develops from one abnormal smooth muscle cell of the myometrium, which, as a result of mutation, has the ability to grow unregulated. Common symptoms of the disease include pelvic discomfort or pain, excessive uterine bleeding, secondary anemia, infertility, and bowel and bladder dysfunction. Uterine fibroids can negatively affect the general condition of a woman, causing hormonal, vegetative-vascular and psycho-emotional disorders. Moreover, about 80% of patients with uterine fibroids undergo radical surgical treatment. The mechanisms of development and growth of this benign tumor have not been fully established and remain controversial. The role of immune disorders in the pathogenesis of fibroids is currently being discussed. It has been proven that the growth of fibroids is accompanied by a weakening of immune defense against the background of an increase in the level of proinflammatory cytokines, which are regulators of the processes of proliferation and apoptosis, mediators of the action of sex steroids. The aim of the study was to study the level of some pro-inflammatory cytokines (IL-1β, IL-2, TGF-β2 and MCP-1) in the blood serum of women of reproductive age with uterine fibroids, depending on the size and nature of tumor growth. A comparative analysis of the results of examination of 123 women with uterine fibroids, who made up 2 groups: 1st group of 65 women with simple uterine fibroids and 2nd group of 58 women with rapidly growing uterine fibroids, is presented. The examination included a comprehensive clinical and laboratory study. The level of cytokines (IL-1β, IL- 2, TGF-β2, MCP-1) in the blood serum was assessed by ELISA. It was found that in patients of reproductive age with uterine fibroids, there is an increase in the levels of IL-1β, TGF-β2 and MCP-1. In women with rapidly growing uterine fibroids, these changes are more pronounced. The concentration of IL-2 is reduced in all women with uterine fibroids, regardless of the size and nature of tumor growth. It has been established that an increase in the size of myomatous nodes is accompanied by an increase in immune imbalance: there is an increase in the levels of pro-inflammatory cytokines (IL-1β, TGF-β2, MCP-1) against the background of a reduced content of the lymphokine IL-2, which we regard as possible links in the pathogenesis of uterine fibroids. A pronounced deficiency of IL-2 in the blood serum against the background of a sharp increase in the concentration of TGF-β2 and MCP-1 in the blood of women with uterine fibroids can be considered as a negative criterion for predicting disease progression and used as an additional prognostic marker of tumor growth

Unmasking Neuroendocrine Prostate Cancer with a Machine Learning-Driven Seven-Gene Stemness Signature That Predicts Progression

Prostate cancer (PCa) poses a significant global health challenge, particularly due to its progression into aggressive forms like neuroendocrine prostate cancer (NEPC). This study developed and validated a stemness-associated gene signature using advanced machine learning techniques, including Random Forest and Lasso regression, applied to large-scale transcriptomic datasets. The resulting seven-gene signature (KMT5C, DPP4, TYMS, CDC25B, IRF5, MEN1, and DNMT3B) was validated across independent cohorts and patient-derived xenograft (PDX) models. This signature demonstrated strong prognostic value for progression-free, disease-free, relapse-free, metastasis-free, and overall survival. Importantly, the signature not only identified specific NEPC subtypes, such as large-cell neuroendocrine carcinoma, which is associated with very poor outcomes, but also predicted a poor prognosis for PCa cases that exhibit this molecular signature, even when they were not histopathologically classified as NEPC. This dual prognostic and classifier capability makes the seven-gene signature a robust tool for personalized medicine, providing a valuable resource for predicting disease progression and guiding treatment strategies in PCa management.

Exploring diagnostic biomarkers of type 2 cardio-renal syndrome based on secreted proteins and bioinformatics analysis

Chronic heart failure (CHF) can induce chronic kidney disease (CKD), called type 2 cardio-renal syndrome (CRS2). The mechanism is not completely clear, and there is a lack of early warning biomarkers of CKD in the context of CHF. Two CKD, one CHF-PBMC and four CHF-cardiac tissue expression profile datasets were obtained from GEO database. Differential expression analysis and WGCNA were used to detect CKD key genes and CHF-related secreted proteins. Protein–protein interaction (PPI), functional enrichment, and cMAP analysis reveal potential mechanisms and drugs of CHF-related CKD. Five machine learning algorithms were used to screen candidate biomarkers, construct a diagnostic nomogram for CKD and validate it in two external cohorts. Clinical serum samples were collected in our hospital to evaluate the correlation and diagnostic value of biomarkers and CKD. 225 CKD key genes and 316 CHF-related secreted proteins were identified. Four key subgroups, including 204 genes, were identified as CRS2-related pathogenic genes by PPI analysis. Enrichment analysis revealed that the identified subgroups exhibited significant enrichment in cytokine action, immune responses, and inflammatory processes. The cMAP analysis highlighted metiradone as a drug with greater potential for therapeutic intervention for CRS2. Utilizing five machine learning algorithms, three hub genes (CD48, COL3A1, LOXL1) were pinpointed as potential biomarkers for CKD, and a nomogram model was constructed. Receiver operating characteristic analysis demonstrated that the nomogram’s area under the curve (AUC) exceeded 0.80 in both the CKD combined dataset and two external cohorts. In addition, the three biomarkers were significantly correlated with the glomerular filtration rate, and the AUC of the model predicting disease progression was 0.944. Furthermore, analysis of immune cell infiltration indicated a correlation between the three biomarkers and the infiltration fraction of macrophages, neutrophils, and other immune cells in CKD. Our clinical cohort validated the expression patterns of three biomarkers in serum, and the diagnostic model achieved an AUC of 0.876. CHF has the potential to facilitate the progression of CKD via the release of cardiac and PBMC secreted proteins. Furthermore, CD48, COL3A1, and LOXL1 have been identified as potential biomarkers for the detection of CKD.

Trajectories of disease evolution upon treatment initiation in systemic lupus erythematosus: results from four clinical trials of belimumab.

Upon commencement of therapy for active disease, patients with systemic lupus erythematosus (SLE) show varying evolution regarding disease activity measures and patient-reported outcomes (PROs). Our objective was to identify disease evolution trajectories to gain a deeper understanding of SLE progression, ultimately improving future trial design. Patients with ≥2 visits and available data on SLEDAI-2K, BILAG, PGA, FACIT-F, and glucocorticoid use were included in a post-hoc analysis of four randomised controlled trials of belimumab (BLISS-52, BLISS-76, BLISS-SC, EMBRACE). Growth mixture modelling identified latent classes. Among 2868 patients analysed, baseline median disease duration was 4.5 (IQR: 1.5-9.7) years and mean (± standard deviation) SDI 0.7 (±2.0), SLEDAI-2K 10.2 (±3.6), BILAG 17.0 (±7.8), PGA 1.5 (±0.5), FACIT-F 30.6 (±11.9), and prednisone dose 11.0 (±8.9) mg/day. In the initial model, glucocorticoid use and dose yielded high standard errors, indicating a weak link with the latent process. A refined model considered only clinical measures and FACIT-F, corrected for intervention and SDI; no other covariates improved the fit. Four classes best described disease evolution: highly active, responders; highly active, non-responders; moderately active, responders; moderately active, non-responders. LLDAS and DORIS remission attainment associated with latent classes. By linking disease activity measures with PROs, we identified four distinct trajectories describing SLE evolution following the initiation of therapy. This classification could be valuable for personalising treatment and guiding biological studies aimed at distinguishing patients with varying anticipated treatment responses, as no single clinical variable alone can predict disease progression.

Rare variants and survival of patients with idiopathic pulmonary fibrosis.

The clinical course of idiopathic pulmonary fibrosis (IPF) is highly variable and unpredictable, with multiple genetic variants influencing IPF outcomes. Notably, rare pathogenic variants in telomere-related genes are associated with poorer clinical outcomes in these patients. Here we assessed whether rare qualifying variants (QVs) in monogenic adult-onset pulmonary fibrosis (PF) genes are associated with IPF survival. Using polygenic risk scores (PRS), we also evaluated the influence of common IPF risk variants in individuals carrying these QVs. We identified QVs in telomere and non-telomere genes linked to monogenic PF forms using whole-genome sequences (WGS) from 888 Pulmonary Fibrosis Foundation Patient Registry (PFFPR) individuals. We also derived a PRS for IPF (PRS-IPF) from 19 previously published common sentinel IPF variants. Using regression models, we then examined the mutual relationships of QVs and PRS-IPF and their association with survival. Validation of results was sought in WGS from an independent IPF study (PROFILE, n=472), and results from the two cohorts were meta-analyzed. Carriers of QVs in monogenic adult-onset PF genes, representing nearly 1 out of 6 IPF patients, were associated with lower PRS-IPF (Odds Ratio [OR]: 1.79; 95% Confidence Interval [CI]: 1.15-2.81; p=0.010) and shorter survival (Hazard Ratio [HR]: 1.53; 95% CI: 1.12-2.10; p=7.3×10 -3 ). Notably, carriers of pathogenic variants at telomere genes showed the strongest association with survival (HR: 1.76; 95% CI: 1.13-2.76; p=0.013). The meta-analysis of the results showed a consistent direction of effect across both cohorts. We revealed the opposite effects of QVs and PRS-IPF on IPF survival. Thus, a distinct IPF molecular subtype might be defined by QVs in monogenic adult-onset PF genes. Assessing the carrier status for QVs and modelling PRS-IPF promises to further contribute to predicting disease progression among IPF patients.

Prognostic Impact of H19/Cell Adhesion Molecules Circuitry on Prostate Cancer Biopsy.

Metastatic prostate cancer (PCa) presents a significant challenge in oncology due to its high mortality rate and the absence of effective biomarkers for predicting patient outcomes. Building on previous research that highlighted the critical role of the long noncoding RNA (lncRNA) H19 and cell adhesion molecules in promoting tumor progression under hypoxia and estrogen stimulation, this study aimed to assess the potential of these components as prognostic biomarkers for PCa at the biopsy stage. This research utilized immunohistochemistry and droplet digital PCR to analyze formalin-fixed paraffin-embedded (FFPE) biopsies, focusing on specific markers within the H19/cell adhesion molecules pathway. A novel multivariate analysis led to a "BioScore", a composite biomarker score to predict disease progression. This score is based on evaluating five key markers: the expression levels of Hypoxia-Inducible Factor 2 Alpha (HIF-2α), endothelial Nitric Oxide Synthase (eNOS), β4 integrin, E-cadherin transcript (CDH1), and lncRNA H19. The criteria for the "BioScore" involve identifying three out of these five markers, combining elevated levels of HIF-2α, eNOS, β4 integrin, and CDH1 with reduced H19 expression. This finding suggests the possibility of identifying, at the time of biopsy, PCa patients at higher risk of metastasis based on dysregulation in the H19/cell adhesion molecules circuitry. This study provides a valuable opportunity for early intervention in managing PCa, potentially contributing to personalized treatment strategies.

18F]NaF PET/CT as a Marker for Fibrodysplasia Ossificans Progressiva: From Molecular Mechanisms to Clinical Applications in Bone Disorders.

Fibrodysplasia ossificans progressiva (FOP) is a rare genetic bone disorder characterized by episodic flare-ups in connective tissue, which are frequently followed by the formation of heterotopic ossification. The absence of available plasma-soluble biomarkers for flare-ups or heterotopic bone formation poses severe challenges to the monitoring of disease activity to measure or predict disease progression. Recently, 18-fluor-sodium fluoride positron emission tomography/computed tomography ([18F]NaF PET/CT) was introduced as a potential marker for ossifying FOP activity. This review discusses the pharmacokinetics of [18F]NaF in relation to the pathophysiology of FOP, and its use as a marker of local bone metabolism in a variety of bone-related disorders. In addition, the review specifically addresses the applicability of [18F]NaF PET/CT imaging in FOP as a monitoring modality.

Exploring serum bile acids as potential noninvasive biomarkers for nonalcoholic fatty liver disease

BackgroundBile acids are vital regulators of liver metabolism, and their dysregulation is closely linked with the progression of nonalcoholic fatty liver disease (NAFLD). Profiling these bile acids may provide valuable diagnostic and prognostic markers for these conditions. This study aimed to evaluate bile acid profiles in NAFLD patients and assess their potential as biomarkers for diagnosing and predicting disease progression. Serum levels of 14 bile acids were measured in 25 normal healthy controls (NHC), 35patients with metabolic dysfunction–associated steatotic liver disease (MASLD), and 40 patients with NASH, categorized by the NAFLD Activity Score (NAS). Quantification was performed using high-performance liquid chromatography-tandem mass spectrometry (HPLC–MS/MS).ResultsPrimary unconjugated bile acids, CA and CDCA, along with conjugated acids GCA, GCDCA, TCA, and TCDCA, were significantly elevated in both MASLD and NASH compared to NHC (all p < 0.05). While levels increased progressively from NHC to MASLD to NASH, no significant differences were observed between MASLD and NASH except for GCA and TCA (P < 0.05). Similarly, secondary bile acids LCA, TLCA, GUDCA, and TUDCA were higher in MASLD and NASH compared to NHC (all p < 0.05).Logistic regression identified CA (odds ratio = 2.05, p = 0.02), CDCA (odds ratio = 1.58, p = 0.04), GCA (odds ratio = 1.92, p = 0.03) and DCA (odds ratio = 2.06, p = 0.04) as significant predictors of fibrosis. For active inflammation, GCA (odds ratio = 2.04, p = 0.04), and TCA (odds ratio = 1.94, p = 0.04) were significant predictors. In steatosis, CA, CDCA, GCA, DCA, TDCA, TLCA, and UDCA were notable predictors, with high odds ratios.ConclusionThe study highlights significant alterations in bile acid profiles associated with NAFLD progression. Specific bile acids, such as CA, GCA, TCA, and TCDCA are strong predictors of disease severity, indicating their potential as biomarkers for NAFLD treatment and prognosis.

A-311 Evaluating the Predictive Value of the sFlt-1/PlGF Ratio in Hypertensive Pregnancies for Preeclampsia

Abstract Background Preeclampsia (PE) is a hypertensive pregnancy disorder affecting around 5% of all pregnancies, and it continues to be a leading cause of maternal and neonatal morbidity and mortality in the United States. The incidence of hypertensive pregnancies is on the rise, attributed to the increasing prevalence of obesity and chronic hypertension. The development of a straightforward diagnostic test to accurately differentiate PE from gestational or chronic hypertension is crucial to avoid unnecessary hospitalizations. Despite the ongoing development of prediction models for PE, conventional clinical and laboratory measures have not been effective in predicting disease progression. Recent research has focused on the ratio of two serum biomarkers: soluble fms-like tyrosine kinase-1 (sFlt-1) and placental growth factor (PlGF), particularly in patients with a clinical suspicion of PE in acute settings. These studies have shown promise, with many indicating that the sFlt-1/PlGF ratio can effectively predict the presence or absence of PE. This study was conducted to assess the predictive value of the sFlt-1/PlGF ratio in pregnancies complicated by hypertension, aiming to contribute to the early detection and management of PE. Methods Pregnant individuals with hypertensive disorders at or above the age of 18 receiving prenatal care at NewYork-Presbysterian-Weill Cornell Hospital were enrolled in this study. We measured the levels of sFlt-1 and PlGF using fully automated Elecsys immunoassays (Roche Diagnostics). Subsequently, a prospective analysis of the serum sFlt-1/PlGF ratio was performed. A receiver operating characteristic (ROC) curve was used to calculate the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the ratio in predicting PE. To assess the prognostic accuracy of the sFlt-1/PlGF ratio, patients were randomly assigned to a development and a validation cohort at a ratio of 30:70. Results We collected a total of 137 serum samples of hypertensive pregnancies, with all ≥20 weeks of gestational age (GA). Of these, 47 (34.3%) developed PE within 2 weeks. The samples were divided into two cohorts: 98 were included in the development cohort, and 39 in the validation cohort. Within these groups, 34 (34.7%) of the development cohort and 13 (33.3%) of the validation cohort developed PE within 2 weeks, respectively. In the development cohort, the median sFlt-1/PlGF ratio was 85 (Interquartile range, [IQR], 44-164) among pregnant women who developed PE within 2 weeks. This was significantly higher compared to a median ratio of 8 (IQR, 3-35) observed in those who did not develop PE (p&amp;lt;0.001). The area under the ROC curve was 0.87, indicating good discriminative ability. An optimized cutoff ratio of 38 was determined, based on the max of combined sensitivity and specificity. When applied into the validation cohort, this cutoff yields a 90% (95% confidence interval [CI], 70-97) NPV and 58% (95%CI, 37-76) PPV with an 85% sensitivity (95%CI, 58-96) and a 69% specificity (95%CI, 50-83). Conclusions In women with a hypertensive disorder of pregnancy presenting GA≥20 weeks of gestation, the measurement of serum sFlt-1/PlGF ratio demonstrated clinical value in predicting the development of PE.

B-274 Determining Novel Biomarker Candidates Associated With Heroin and Cocaine Exposure Using Urine Metabolomics

Abstract Background Substance abuse poses an escalating health risk in the United States. While acute drug exposure can be tracked using drugs and their metabolites as biomarkers in clinical settings, there is a notable absence of biomarkers for chronic drug exposure or addiction. The development and availability of such biomarkers could significantly enhance clinicians’ ability to identify patients at high risk of addiction, and more accurately predict disease progression and treatment responses. Methods Our study aimed to discover novel biomarkers associated with substance abuse by analyzing raw mass spectrometry (MS) data from urine comprehensive drug screening (UCDS) conducted at the University of Pittsburgh Medical Center (UPMC). The MS data, acquired through untargeted data collection using LC-qToF-MS at the UPMC Clinical Toxicology Laboratory, was initially preprocessed (including peak identification, alignment, and putative annotation) using MS-DIAL to generate a data matrix. This matrix, consisting of objects (cases) and features (m/z and retention time), was further normalized and analyzed using MetaboAnalyst. A combination of statistical methods, including point biserial correlation analysis, volcano plot, significance analysis of microarrays and metabolites (SAM), empirical Bayesian analysis of microarrays and metabolites (EBAM), partial least squares discriminant analysis (PLS-DA), and random forest (RF), were employed to select features significantly associated with the EIA-results. These features were subsequently evaluated for analyte identification using MS-FINDER. Results Our analysis identified nine significant features associated with 6MAM-EIA for heroin abuse, including 6-monoacetylmorphine itself and norfentanyl. For features associated with cocaine metabolite-EIA, 37 significant features were selected, including multiple cocaine metabolites, nicotine metabolites, and norfentanyl. Some of these selected features are likely products of in-source fragmentation or endogenous metabolites. Conclusions Our study has identified a set of potential biomarker candidates for illicit drug exposure. Notably, norfentanyl was found to be significantly associated with both 6MAM-EIA and cocaine metabolite-EIA, reflecting current trends in illicit drug use. Further chemical identification of these biomarkers is planned for future work.

Dynamics of minimal residual disease and its clinical implications in multiple myeloma: A retrospective real-life analysis

BackgroundMinimal residual disease (MRD) testing is a promising approach to tailor the treatment of multiple myeloma (MM). However, several major concerns remain to be addressed before moving it into daily practice, most of which stem from the dynamic nature of the MRD status. Thus, it is crucial to understand the MRD dynamics and propose its clinical implications. MethodsWe retrospectively analysed the data of patients with newly diagnosed MM (NDMM) who had flow cytometry-based MRD tests at multiple time points after initiation of therapy. The impact of undetectable MRD (including attainment, duration and loss) on clinical outcomes was analysed. ResultsIn a cohort of 220 patients with NDMM, attainment of MRD− offered favourable outcomes (P < 0.0001 for both progression-free survival (PFS) and overall survival (OS)), regardless of baseline risk factors. Notably, MRD− duration ≥12 months was associated with an 83 % (95 % confidence interval (CI), 0.09−0.34; P < 0.0001) or 69 % (95 % CI, 0.13−0.76; P = 0.0098) reduction in risk of progression/death or death, while the longer MRD− was sustained, the better the outcome was. Loss of MRD− led to poor PFS (hazard ratio (HR) 0.01, 95 % CI 0–0.06, P < 0.0001) and OS (HR 0.03, 95 % CI 0–0.24, P = 0.0008). Most patients (70 %) who lost MRD− status carried high-risk cytogenetic abnormalities (HRCAs). While MRD− was temporally inconsistent with conventional therapeutic responses (eg ≥ complete remission or very good partial response), it predicted disease progression or recurrence more robustly than the latter. Last, the predictive value of the MRD status was independent of baseline risk factors (eg high-risk cytogenetic abnormality, International Staging System (ISS) or Revised (R-)ISS staging). ConclusionsLongitudinal assessment of MRD during the treatment course and follow-up is required for monitoring disease progression or relapse, to guide treatment decisions. Accordingly, a prospective study is currently ongoing to investigate the feasibility and benefit of the MRD-tailored therapy according to the longitudinal changes of the MRD status.

Predicting Disease Progression in Inoperable Localized NSCLC Patients Using ctDNA Machine Learning Model.

There is an urgent clinical need to accurately predict the risk for disease progression in post-treatment NSCLC patients, yet current ctDNA mutation profiling approaches are limited by low sensitivity. We represent a non-invasive liquid biopsy assay utilizing cfDNA neomer profiling for predicting disease progression in 44 inoperable localized NSCLC patients. A total of 97 plasma samples were collected at various time points during or post-treatments (TP1: 39, TP2: 33, TP3: 25). cfDNA neomer profiling, generated based on target sequencing data, was used to fit survival support vector machine models for each time point. Leave-one-out cross-validation (LOOCV) was performed to evaluate the models' predictive performances. Our cfDNA neomer profiling assay showed excellent performance in detecting patients with a high risk for disease progression. At TP1, the high-risk patients detected by our model showed an increased risk of 3.62 times (hazard ratio [HR] = 3.62, p = 0.0026) for disease progression, compared to 3.91 times (HR = 3.91, p = 0.0022) and 4.00 times (HR = 4.00, p = 0.019) for TP2 and TP3. These neomer profiling determined HRs were higher than the ctDNA mutation-based results (HR = 2.08, p = 0.074; HR = 1.49, p&amp;#x02009;=&amp;#x02009;0.61) at TP1 and TP3. At TP1, the predictive model reached 40% sensitivity at 92.9% specificity, outperforming the mutation-based method (40% sensitivity at 78.6% specificity), while the combination results reached a higher sensitivity (60%). Finally, the longitudinal analysis showed that the combination of neomer and ctDNA mutation-based results could predict disease progression with an excellent sensitivity of 88.9% at 80% specificity. In conclusion, we developed a cfDNA neomer profiling assay for predicting disease progression in inoperable NSCLC patients. This assay showed increased predicting power during and post-treatment compared to the ctDNA mutation-based method, thus illustrating a great clinical potential to guide treatment decisions in inoperable NSCLC patients. ClinicalTrials.gov: NCT04014465.

A comprehensive review of glycocalyx investigation and therapeutic applications in sepsis and septic shock

Abstract Background Sepsis is a global health challenge that causes more than 11 million deaths annually and represents a substantial medical and economic burden. With rising treatment costs and significant mortality rates associated with organ dysfunction and septic shock, research efforts have focused on investigating the mechanism of glycocalyx (GCX) degradation as well as its regenerative capacity. Therefore, GCX has become a target in therapeutic strategies. Methods We performed a comprehensive review of articles published in PubMed database between 2014 and 2024, in the English language, dealing with statistical data, morphological and physiological aspects of the GCX, pathophysiological mechanisms, in vivo and in vitro research methods, clinical and laboratory experiences, therapeutic strategies, and innovative methods of prevention, both in the context of sepsis and its associated complications. Results The database search identified 300 records on the topic. After title/abstract screening, 187 articles were assessed in full text for eligibility, including articles with additional topics addressing the main topic. Of these, a total of 70 studies were included. Conclusions Exploring the structure of GCX holds real potential in the diagnosis and treatment of sepsis and its complications. Current research focuses on understanding GCX degradation, correlating its components with sepsis severity, predicting disease progression, and evaluating the impact of therapeutic strategies on GCX components.

Identifying clinical and proteomic markers for early diagnosis and prognosis prediction of major psychiatric disorders

BackgroundTo clarify if blood proteins can predict disease progression among individuals at clinical high-risk of severe mental illness (CHR-SMI), we developed a statistical model incorporating clinical and blood protein markers to distinguish the transition group (who developed severe mental illness) (CHR-SMI-T) and from non-transition group (CHR-SMI-NT) at baseline. MethodsNinety individuals (74 at CHR-SMI: 16 patients) were monitored for ≤4 years and were the focus of predictive models. Three predictive models (1 [100 clinical variables], 2 [158 peptides], and 3 [100 clinical variables +158 peptides]) were evaluated using area under the receiver operating characteristic (AUROC) values. Clinical and protein feature patterns were evaluated by linear mixed-effect analysis within the model at 12 and 24 months among patients who did (CHR-SMI-T) and did not transition (CHR-SMI-NT) and the entire group. ResultEighteen CHR-SMI individuals with major psychiatric disorders (first episode psychosis: 2; bipolar II disorder: 13; major depressive disorder; 3) developed disorders over an average of 17.7 months. The combined model showed the highest discriminatory performance (AUROC = 0.73). Cytosolic malate dehydrogenase and transgelin-2 levels were lower in the CHR-SMI-T than the CHR-SMI-NT group. Complement component C9, inter-alpha-trypsin inhibitor heavy chain H4, von Willebrand factor, and C-reactive protein were lower in the patient than the CHR-SMI-NT group. These differences were non-significant after FDR adjustment. LimitationsSmall sample, no control for medication use. ConclusionThis exploratory study identified clinical and proteomic markers that might predict severe mental illness early onset, which could aid in early detection and intervention. Future studies with larger samples and controlled variables are needed to validate these findings.

Human papillomavirus self-sampling and urine-sampling tests and the management and short-term outcomes of cervical intraepithelial neoplasia: A prospective observational study.

The importance of human papillomavirus (HPV) co-testing using physician-, self-, and urine-collected samples to predict cervical intraepithelial neoplasia (CIN) grade 1-2 prognoses has not been previously reported. Therefore, this study aimed to investigate outcomes of patients with CIN 1-2 who simultaneously underwent physician-, self-, and urine-collection sampling tests. This study was conducted in Japan between October 2019 and November 2022 and examined the proportion of cases with CIN 1-2 progressions, the percentage of cases with persistent CIN 1-2, and the outcome differences according to the results of physician-, self-, and urine-sampling tests. There were 105 and 59 CIN 1 and 2 cases, respectively, with progression or persistence in 27 (29.3%) and 21 (50.0%) cases, respectively. The median follow-up was 20 and 12 months, respectively. Progression and persistence of CIN 1 were significantly associated with HPV-positive physician- and self-collected samples. No significant difference was observed between cases with CIN 2 who had HPV-positive and HPV-negative results using any sampling method. Physician- and self-testing for HPV are crucial for predicting disease progression risk in CIN 1 cases. Future research with an extended observation period and consideration of the progression risks is warranted.

OA03.04 Predicting Disease Progression to Upfront Pembrolizumab Monotherapy in Advanced NSCLC with Machine Learning and CT Radiomics

OA03.04 Predicting Disease Progression to Upfront Pembrolizumab Monotherapy in Advanced NSCLC with Machine Learning and CT Radiomics

Combined cognitive assessment and automated MRI volumetry improves the diagnostic accuracy of detecting MCI due to Alzheimer's disease

BackgroundMild cognitive impairment (MCI) confers a high annual risk of 10–15 % of conversion to Alzheimer's disease (AD) dementia. MRI atrophy patterns derived from automated ROI analysis, particularly hippocampal subfield volumes, were reported to be useful in diagnosing early clinical stages of Alzheimer's disease. ObjectiveThe aim of the present study was to combine automated ROI MRI morphometry of hippocampal subfield volumes and cortical thickness estimates using FreeSurfer 6.0 with cognitive measures to predict disease progression and time to conversion from MCI to AD dementia. MethodsBaseline (Neuropsychology, MRI) and clinical follow-up data from 62 MCI patients were analysed retrospectively. Individual cortical thickness and volumetric measures were obtained from T1-weighted MRI. Linear discriminant analysis (LDA) of both, cognitive measures and MRI measures (hippocampal subfields, temporal and parietal lobe volumes), were performed to differentiate MCI converters from stable MCI patients. ResultsOut of 62 MCI patients 21 (34 %) converted to AD dementia within a mean follow-up time of 74.7 ± 36.8 months (mean ± SD, range 12 to 130 months). LDA identified temporal lobe atrophy and hippocampal subfield volumes in combination with cognitive measures of verbal memory, verbal fluency and executive functions to correctly classify 71.4.% of MCI subjects converting to AD dementia and 92.7 % with stable MCI. Lower baseline GM volume of the subiculum and the superior temporal gyrus was associated with faster disease progression of MCI converters. ConclusionCombining cognitive assessment with automated ROI MRI morphometry is superior to using a single test in order to distinguish MCI due to AD from non converting MCI patients.

Predicting the Path: How Machine Learning Can Identify Subtypes of Epilepsy and Predict Disease Progression

Predicting the Path: How Machine Learning Can Identify Subtypes of Epilepsy and Predict Disease Progression